Topic: COVID-19 New cases visualisation dashboard: Shiny app

covid = read.csv("owid-covid-data.csv")

# these are indices for each country (an index is just a collection of stocks)
# 'FileEncoding' just cleans the column encoding for this case

#sp500 = read.csv("SPY Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for US
#TOPIX = read.csv("TOPIX Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for Japan
ASX200 = read.csv("S&P_ASX 200 Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for Australia
#NSEI = read.csv("Nifty 50 Historical Data.csv", fileEncoding = 'UTF-8-BOM') # This is for India
#SSEC = read.csv("Shanghai Composite Historical Data.csv", fileEncoding = 'UTF-8-BOM') # this is for China

colnames(covid)
##  [1] "iso_code"                                  
##  [2] "continent"                                 
##  [3] "location"                                  
##  [4] "date"                                      
##  [5] "total_cases"                               
##  [6] "new_cases"                                 
##  [7] "new_cases_smoothed"                        
##  [8] "total_deaths"                              
##  [9] "new_deaths"                                
## [10] "new_deaths_smoothed"                       
## [11] "total_cases_per_million"                   
## [12] "new_cases_per_million"                     
## [13] "new_cases_smoothed_per_million"            
## [14] "total_deaths_per_million"                  
## [15] "new_deaths_per_million"                    
## [16] "new_deaths_smoothed_per_million"           
## [17] "reproduction_rate"                         
## [18] "icu_patients"                              
## [19] "icu_patients_per_million"                  
## [20] "hosp_patients"                             
## [21] "hosp_patients_per_million"                 
## [22] "weekly_icu_admissions"                     
## [23] "weekly_icu_admissions_per_million"         
## [24] "weekly_hosp_admissions"                    
## [25] "weekly_hosp_admissions_per_million"        
## [26] "total_tests"                               
## [27] "new_tests"                                 
## [28] "total_tests_per_thousand"                  
## [29] "new_tests_per_thousand"                    
## [30] "new_tests_smoothed"                        
## [31] "new_tests_smoothed_per_thousand"           
## [32] "positive_rate"                             
## [33] "tests_per_case"                            
## [34] "tests_units"                               
## [35] "total_vaccinations"                        
## [36] "people_vaccinated"                         
## [37] "people_fully_vaccinated"                   
## [38] "total_boosters"                            
## [39] "new_vaccinations"                          
## [40] "new_vaccinations_smoothed"                 
## [41] "total_vaccinations_per_hundred"            
## [42] "people_vaccinated_per_hundred"             
## [43] "people_fully_vaccinated_per_hundred"       
## [44] "total_boosters_per_hundred"                
## [45] "new_vaccinations_smoothed_per_million"     
## [46] "new_people_vaccinated_smoothed"            
## [47] "new_people_vaccinated_smoothed_per_hundred"
## [48] "stringency_index"                          
## [49] "population"                                
## [50] "population_density"                        
## [51] "median_age"                                
## [52] "aged_65_older"                             
## [53] "aged_70_older"                             
## [54] "gdp_per_capita"                            
## [55] "extreme_poverty"                           
## [56] "cardiovasc_death_rate"                     
## [57] "diabetes_prevalence"                       
## [58] "female_smokers"                            
## [59] "male_smokers"                              
## [60] "handwashing_facilities"                    
## [61] "hospital_beds_per_thousand"                
## [62] "life_expectancy"                           
## [63] "human_development_index"                   
## [64] "excess_mortality_cumulative_absolute"      
## [65] "excess_mortality_cumulative"               
## [66] "excess_mortality"                          
## [67] "excess_mortality_cumulative_per_million"

data clean

#Dropping NA
covid_clean = covid %>% drop_na(new_cases, new_cases_smoothed, new_vaccinations, new_vaccinations_smoothed, new_vaccinations_smoothed_per_million, population, population_density, median_age, extreme_poverty, total_vaccinations, hospital_beds_per_thousand, human_development_index, new_deaths, new_tests, weekly_icu_admissions_per_million, weekly_icu_admissions)

#This code changes the negative case values in the data set to zero
covid_clean$new_cases[covid_clean$new_cases < 0] <- 0

covid_AUS <- covid %>% filter(location == "Australia")
covid_clean_AUS = covid_AUS %>% drop_na(new_cases, new_cases_smoothed, new_vaccinations, new_vaccinations_smoothed, new_vaccinations_smoothed_per_million, population, population_density, median_age, extreme_poverty, total_vaccinations, hospital_beds_per_thousand, human_development_index, new_deaths, new_tests)

#This code changes the negative case values in the data set to zero
covid_clean_AUS$new_cases[covid_clean_AUS$new_cases < 0] <- 0
view(covid_clean_AUS)

#glimpse(covid_clean)
#covid_clean$date = as.Date(covid_clean$date)
#max(covid_clean$date)
#unique(covid_clean$location)

Question 1

#code for more optimised join (to be pasted later)
class(covid_clean$date)
## [1] "character"

performing join

# using a copy just in case
covid2 <- covid_clean_AUS

#sp500$Date = mdy(sp500$Date)
#TOPIX$Date = mdy(TOPIX$Date)
ASX200$date = mdy(ASX200$Date)
#NSEI$Date = mdy(NSEI$Date)
#SSEC$Date = mdy(SSEC$Date)

#sp500$date = mdy(sp500$Date)
#TOPIX$date = mdy(TOPIX$Date)
ASX200$date = mdy(ASX200$Date)
#NSEI$date = mdy(NSEI$Date)
#SSEC$date = mdy(SSEC$Date)

covid2$date = ymd(covid2$date)

#Temporarily changing the date to character as joining cannot be done with date objects
#Also selecting relevant columns for analysis later
covid2 <- covid2 %>%
  transform(covid2, date = as.character(date)) %>% 
  select(date, new_cases, new_deaths, location, new_vaccinations, new_tests, population, population_density)

#sp500$Date <- as.character(sp500$Date)
#TOPIX$Date <- as.character(TOPIX$Date)
ASX200$date <- as.character(ASX200$date)
#NSEI$Date <- as.character(NSEI$Date)
#SSEC$Date <- as.character(SSEC$Date)

#sp500$date <- as.character(sp500$Date)
#TOPIX$date <- as.character(TOPIX$Date)
ASX200$date <- as.character(ASX200$date)
#NSEI$date <- as.character(NSEI$Date)
#SSEC$date <- as.character(SSEC$Date)

# renaming column so it has same name as the stock market data frames for joining later
#colnames(covid2)[1] = "Date"

# making data frames for each country we select to perform individual joins on each to their respective stock market index
#covid_US <- covid2 %>% filter(location == "United States")
covid_AUS <- covid2 %>% filter(location == "Australia")
#covid_IND <- covid2 %>% filter(location == "India")
#covid_JPN <- covid2 %>% filter(location == "Japan")
#covid_CHN <- covid2 %>% filter(location == "China")

# performing joins 

#df_1 = inner_join(sp500, covid_US, by = "Date")
#df_2 = inner_join(TOPIX, covid_JPN, by = "Date")
df_3 = inner_join(ASX200, covid_AUS, by = "date")
#df_4 = inner_join(NSEI, covid_IND, by = "Date")
#df_5 = inner_join(SSEC, covid_CHN, by = "Date")

#df_1 = inner_join(sp500, covid_US, by = "date")
#df_2 = inner_join(TOPIX, covid_JPN, by = "date")
df_3 = inner_join(ASX200, covid_AUS, by = "date")
#df_4 = inner_join(NSEI, covid_IND, by = "date")
#df_5 = inner_join(SSEC, covid_CHN, by = "date")


# vertically joined data set (now one column will store all the values of the respective country index)
# e.g US stores prices relevant to S&P500 and China's prices are relevant to the the SSEC which is based in Shanghai.
#covid_joined <- rbind(df_1, df_2, df_3, df_4, df_5)

# Still need transform relevant column to numeric ect...will do a little later 

df_aus <- df_3

#transformation
df_aus$date = as.Date(df_aus$date)
df_aus$Price = as.numeric(gsub(",","",df_aus$Price))

df_aus
##             Date  Price     Open     High      Low    Vol. Change..       date
## 1   Mar 25, 2022 7406.2 7,387.10 7,431.30 7,387.10 679.65M    0.26% 2022-03-25
## 2   Mar 24, 2022 7387.1 7,377.90 7,399.40 7,356.20 633.60M    0.12% 2022-03-24
## 3   Mar 23, 2022 7377.9 7,341.10 7,386.90 7,329.80 575.42M    0.50% 2022-03-23
## 4   Mar 22, 2022 7341.1 7,278.50 7,377.30 7,278.50 634.67M    0.86% 2022-03-22
## 5   Mar 21, 2022 7278.5 7,294.40 7,350.40 7,278.50 561.98M   -0.22% 2022-03-21
## 6   Mar 18, 2022 7294.4 7,250.80 7,294.40 7,245.90   1.54B    0.60% 2022-03-18
## 7   Mar 17, 2022 7250.8 7,175.20 7,296.80 7,175.20 831.38M    1.05% 2022-03-17
## 8   Mar 16, 2022 7175.2 7,097.40 7,180.20 7,097.40 701.73M    1.10% 2022-03-16
## 9   Mar 15, 2022 7097.4 7,149.40 7,149.40 7,080.70 788.37M   -0.73% 2022-03-15
## 10  Mar 14, 2022 7149.4 7,063.60 7,149.40 7,063.60 599.30M    1.21% 2022-03-14
## 11  Mar 11, 2022 7063.6 7,130.80 7,151.40 7,051.20 816.85M   -0.94% 2022-03-11
## 12  Mar 10, 2022 7130.8 7,053.00 7,161.00 7,039.70 976.57M    1.10% 2022-03-10
## 13  Mar 09, 2022 7053.0 6,980.30 7,072.00 6,968.90   1.04B    1.04% 2022-03-09
## 14  Mar 08, 2022 6980.3 7,038.60 7,053.50 6,980.30 973.85M   -0.83% 2022-03-08
## 15  Mar 07, 2022 7038.6 7,110.80 7,133.70 7,010.50   1.02B   -1.02% 2022-03-07
## 16  Mar 04, 2022 7110.8 7,151.40 7,151.40 7,025.20 972.29M   -0.57% 2022-03-04
## 17  Mar 03, 2022 7151.4 7,116.70 7,198.10 7,116.70 856.24M    0.49% 2022-03-03
## 18  Mar 02, 2022 7116.7 7,096.50 7,121.50 7,041.90 941.03M    0.28% 2022-03-02
## 19  Mar 01, 2022 7096.5 7,049.10 7,159.90 7,049.10 812.87M    0.67% 2022-03-01
## 20  Feb 28, 2022 7049.1 6,997.80 7,049.60 6,979.40   1.01B    0.73% 2022-02-28
## 21  Feb 25, 2022 6997.8 6,990.60 7,045.60 6,974.90 888.81M    0.10% 2022-02-25
## 22  Feb 24, 2022 6990.6 7,205.70 7,205.70 6,959.30   1.11B   -2.99% 2022-02-24
## 23  Feb 23, 2022 7205.7 7,161.30 7,205.70 7,145.40 788.38M    0.62% 2022-02-23
## 24  Feb 15, 2022 7206.9 7,243.90 7,251.40 7,198.00 730.13M   -0.51% 2022-02-15
## 25  Feb 14, 2022 7243.9 7,217.30 7,263.60 7,181.70 749.35M    0.37% 2022-02-14
## 26  Feb 11, 2022 7217.3 7,288.50 7,294.40 7,191.80 629.39M   -0.98% 2022-02-11
## 27  Feb 10, 2022 7288.5 7,268.30 7,336.60 7,260.80 886.64M    0.28% 2022-02-10
## 28  Feb 09, 2022 7268.3 7,186.70 7,268.30 7,183.00 978.75M    1.14% 2022-02-09
## 29  Feb 08, 2022 7186.7 7,110.80 7,203.00 7,110.80 771.03M    1.07% 2022-02-08
## 30  Feb 07, 2022 7110.8 7,120.20 7,128.30 7,046.50 617.94M   -0.13% 2022-02-07
## 31  Jan 28, 2022 6988.1 6,838.30 7,000.00 6,837.90   2.09B    2.19% 2022-01-28
## 32  Jan 27, 2022 6838.3 6,961.60 7,042.80 6,758.20   1.18B   -1.77% 2022-01-27
## 33  Jan 25, 2022 6961.6 7,139.50 7,139.50 6,920.70   1.10B   -2.49% 2022-01-25
## 34  Jan 24, 2022 7139.5 7,173.70 7,173.70 7,086.80 760.46M   -0.51% 2022-01-24
## 35  Jan 21, 2022 7175.8 7,342.40 7,342.40 7,153.30 910.99M   -2.27% 2022-01-21
## 36  Jan 20, 2022 7342.4 7,332.50 7,354.60 7,298.50 684.95M    0.14% 2022-01-20
## 37  Jan 19, 2022 7332.5 7,408.80 7,408.80 7,325.70 697.49M   -1.03% 2022-01-19
## 38  Jan 18, 2022 7408.8 7,417.30 7,445.10 7,398.20 549.27M   -0.11% 2022-01-18
## 39  Jan 17, 2022 7417.3 7,393.90 7,429.30 7,385.80 472.82M    0.32% 2022-01-17
## 40  Jan 14, 2022 7393.9 7,474.40 7,474.40 7,386.80 619.98M   -1.08% 2022-01-14
## 41  Jan 13, 2022 7474.4 7,438.90 7,487.30 7,438.90 605.24M    0.48% 2022-01-13
## 42  Jan 12, 2022 7438.9 7,390.10 7,467.50 7,390.10 569.40M    0.66% 2022-01-12
## 43  Jan 11, 2022 7390.1 7,447.10 7,447.10 7,376.80 549.65M   -0.77% 2022-01-11
## 44  Jan 10, 2022 7447.1 7,453.30 7,460.20 7,409.90 410.07M   -0.08% 2022-01-10
## 45  Jan 07, 2022 7453.3 7,358.30 7,484.60 7,358.30 469.08M    1.29% 2022-01-07
## 46  Jan 06, 2022 7358.3 7,565.80 7,565.80 7,340.40 623.92M   -2.74% 2022-01-06
## 47  Jan 05, 2022 7565.8 7,589.80 7,620.20 7,563.40 472.27M   -0.32% 2022-01-05
## 48  Jan 04, 2022 7589.8 7,444.60 7,594.70 7,444.60 503.96M    1.95% 2022-01-04
## 49  Dec 31, 2021 7444.6 7,513.40 7,515.30 7,444.60 272.41M   -0.92% 2021-12-31
## 50  Dec 30, 2021 7513.4 7,509.80 7,520.60 7,499.50 365.35M    0.05% 2021-12-30
## 51  Dec 29, 2021 7509.8 7,420.30 7,518.40 7,420.30 428.61M    1.21% 2021-12-29
## 52  Dec 24, 2021 7420.3 7,387.60 7,435.90 7,387.60 230.00M    0.44% 2021-12-24
## 53  Dec 23, 2021 7387.6 7,364.80 7,404.20 7,363.60 390.03M    0.31% 2021-12-23
## 54  Dec 22, 2021 7364.8 7,355.00 7,374.50 7,333.00 539.85M    0.13% 2021-12-22
## 55  Dec 21, 2021 7355.0 7,292.20 7,358.30 7,287.30 571.41M    0.86% 2021-12-21
## 56  Dec 20, 2021 7292.2 7,304.00 7,304.00 7,257.80 543.60M   -0.16% 2021-12-20
## 57  Dec 17, 2021 7304.0 7,295.70 7,350.30 7,295.70   1.22B    0.11% 2021-12-17
## 58  Dec 16, 2021 7295.7 7,327.10 7,334.50 7,277.10 787.43M   -0.43% 2021-12-16
## 59  Dec 15, 2021 7327.1 7,378.40 7,378.40 7,325.70 514.72M   -0.70% 2021-12-15
## 60  Dec 14, 2021 7378.4 7,379.30 7,393.50 7,341.30 581.39M   -0.01% 2021-12-14
## 61  Dec 13, 2021 7379.3 7,353.50 7,417.70 7,353.50 452.15M    0.35% 2021-12-13
## 62  Dec 10, 2021 7353.5 7,384.50 7,384.50 7,336.70 599.53M   -0.42% 2021-12-10
## 63  Dec 09, 2021 7384.5 7,405.40 7,418.30 7,377.70 542.21M   -0.28% 2021-12-09
## 64  Dec 08, 2021 7405.4 7,313.90 7,443.40 7,313.90 675.57M    1.25% 2021-12-08
## 65  Dec 07, 2021 7313.9 7,245.10 7,326.10 7,245.10 601.67M    0.95% 2021-12-07
## 66  Dec 06, 2021 7245.1 7,241.20 7,257.90 7,207.80 650.43M    0.05% 2021-12-06
## 67  Dec 03, 2021 7241.2 7,225.20 7,288.20 7,211.50 631.36M    0.22% 2021-12-03
## 68  Dec 02, 2021 7225.2 7,235.90 7,239.70 7,168.90 652.44M   -0.15% 2021-12-02
## 69  Dec 01, 2021 7235.9 7,256.00 7,262.30 7,183.40 654.05M   -0.28% 2021-12-01
## 70  Nov 30, 2021 7256.0 7,239.80 7,332.60 7,239.80   1.12B    0.22% 2021-11-30
## 71  Nov 29, 2021 7239.8 7,279.30 7,279.30 7,180.30 817.95M   -0.54% 2021-11-29
## 72  Nov 26, 2021 7279.3 7,407.30 7,407.30 7,260.30 577.17M   -1.73% 2021-11-26
## 73  Nov 25, 2021 7407.3 7,399.40 7,412.70 7,373.70 597.77M    0.11% 2021-11-25
## 74  Nov 24, 2021 7399.4 7,410.60 7,425.00 7,381.80 595.33M   -0.15% 2021-11-24
## 75  Nov 23, 2021 7410.6 7,353.10 7,416.60 7,353.10 606.30M    0.78% 2021-11-23
## 76  Nov 22, 2021 7353.1 7,396.50 7,396.50 7,337.00 486.26M   -0.59% 2021-11-22
## 77  Nov 19, 2021 7396.5 7,379.20 7,404.90 7,376.00 525.25M    0.23% 2021-11-19
## 78  Nov 18, 2021 7379.2 7,369.90 7,398.20 7,343.80 634.51M    0.13% 2021-11-18
## 79  Nov 17, 2021 7369.9 7,420.40 7,431.10 7,342.90 626.09M   -0.68% 2021-11-17
## 80  Nov 16, 2021 7420.4 7,470.10 7,470.10 7,403.50 626.13M   -0.67% 2021-11-16
## 81  Nov 15, 2021 7470.1 7,443.00 7,479.10 7,437.90 499.12M    0.36% 2021-11-15
## 82  Nov 12, 2021 7443.0 7,381.90 7,465.80 7,381.90 572.67M    0.83% 2021-11-12
## 83  Nov 11, 2021 7381.9 7,423.90 7,423.90 7,329.50 601.08M   -0.57% 2021-11-11
## 84  Nov 10, 2021 7423.9 7,434.20 7,460.50 7,411.90 549.06M   -0.14% 2021-11-10
## 85  Nov 09, 2021 7434.2 7,452.20 7,468.60 7,434.20 609.74M   -0.24% 2021-11-09
## 86  Nov 08, 2021 7452.2 7,456.90 7,474.30 7,433.40 581.05M   -0.06% 2021-11-08
## 87  Nov 05, 2021 7456.9 7,428.00 7,477.20 7,424.40 568.02M    0.39% 2021-11-05
## 88  Nov 04, 2021 7428.0 7,392.70 7,428.00 7,392.70 658.11M    0.48% 2021-11-04
## 89  Nov 03, 2021 7392.7 7,324.30 7,431.60 7,324.30 587.77M    0.93% 2021-11-03
## 90  Nov 02, 2021 7324.3 7,370.80 7,395.90 7,311.60 491.50M   -0.63% 2021-11-02
## 91  Nov 01, 2021 7370.8 7,323.70 7,388.30 7,323.70 612.05M    0.64% 2021-11-01
## 92  Oct 29, 2021 7323.7 7,430.40 7,447.00 7,318.80 695.89M   -1.44% 2021-10-29
## 93  Oct 28, 2021 7430.4 7,448.70 7,448.70 7,404.60 578.21M   -0.25% 2021-10-28
## 94  Oct 27, 2021 7448.7 7,443.40 7,473.90 7,419.70 596.69M    0.07% 2021-10-27
## 95  Oct 26, 2021 7443.4 7,441.00 7,471.30 7,439.20 517.77M    0.03% 2021-10-26
## 96  Oct 25, 2021 7441.0 7,415.50 7,471.70 7,415.50 464.11M    0.34% 2021-10-25
## 97  Oct 22, 2021 7415.5 7,415.40 7,431.40 7,391.30 638.59M    0.00% 2021-10-22
## 98  Oct 21, 2021 7415.4 7,413.70 7,446.70 7,403.70 751.88M    0.02% 2021-10-21
## 99  Oct 20, 2021 7413.7 7,374.90 7,449.50 7,374.90 746.76M    0.53% 2021-10-20
## 100 Oct 19, 2021 7374.9 7,381.10 7,406.90 7,373.80 581.73M   -0.08% 2021-10-19
## 101 Oct 18, 2021 7381.1 7,362.00 7,393.80 7,354.10 562.54M    0.26% 2021-10-18
## 102 Oct 15, 2021 7362.0 7,311.70 7,373.20 7,311.70 565.80M    0.69% 2021-10-15
## 103 Oct 14, 2021 7311.7 7,272.50 7,358.60 7,272.50 648.07M    0.54% 2021-10-14
## 104 Oct 13, 2021 7272.5 7,280.70 7,294.00 7,256.30 589.78M   -0.11% 2021-10-13
## 105 Oct 12, 2021 7280.7 7,299.80 7,330.40 7,260.40 669.14M   -0.26% 2021-10-12
## 106 Oct 11, 2021 7299.8 7,320.10 7,320.10 7,250.00 613.01M   -0.28% 2021-10-11
## 107 Oct 08, 2021 7320.1 7,256.70 7,322.00 7,256.70 630.30M    0.87% 2021-10-08
## 108 Oct 07, 2021 7256.7 7,206.50 7,265.20 7,206.50 718.86M    0.70% 2021-10-07
## 109 Oct 06, 2021 7206.5 7,248.40 7,279.40 7,183.50 726.94M   -0.58% 2021-10-06
## 110 Oct 05, 2021 7248.4 7,278.50 7,278.50 7,202.70 735.94M   -0.41% 2021-10-05
## 111 Oct 04, 2021 7278.5 7,185.50 7,305.90 7,185.50 464.15M    1.29% 2021-10-04
## 112 Oct 01, 2021 7185.5 7,332.20 7,332.20 7,157.90 785.10M   -2.00% 2021-10-01
## 113 Sep 30, 2021 7332.2 7,196.70 7,332.20 7,196.70 801.81M    1.88% 2021-09-30
## 114 Sep 29, 2021 7196.7 7,275.60 7,275.60 7,145.70 850.92M   -1.08% 2021-09-29
## 115 Sep 28, 2021 7275.6 7,384.20 7,384.20 7,275.60 753.73M   -1.47% 2021-09-28
## 116 Sep 27, 2021 7384.2 7,342.60 7,416.40 7,342.60 630.60M    0.57% 2021-09-27
## 117 Sep 24, 2021 7342.6 7,370.20 7,377.00 7,334.20 666.26M   -0.37% 2021-09-24
## 118 Sep 23, 2021 7370.2 7,296.90 7,387.00 7,296.90 696.69M    1.00% 2021-09-23
## 119 Sep 22, 2021 7296.9 7,273.80 7,338.60 7,241.80 663.95M    0.32% 2021-09-22
## 120 Sep 21, 2021 7273.8 7,248.20 7,285.90 7,191.70 740.27M    0.35% 2021-09-21
## 121 Sep 20, 2021 7248.2 7,390.40 7,390.40 7,233.60 721.06M   -2.10% 2021-09-20
## 122 Sep 17, 2021 7403.7 7,460.20 7,460.20 7,376.10   1.61B   -0.76% 2021-09-17
## 123 Sep 16, 2021 7460.2 7,417.00 7,487.60 7,417.00 908.93M    0.58% 2021-09-16
## 124 Sep 15, 2021 7417.0 7,437.30 7,437.30 7,378.90 783.02M   -0.27% 2021-09-15
## 125 Sep 14, 2021 7437.3 7,425.20 7,446.00 7,388.90 810.67M    0.16% 2021-09-14
## 126 Sep 13, 2021 7425.2 7,406.60 7,436.80 7,389.40 591.65M    0.25% 2021-09-13
## 127 Sep 10, 2021 7406.6 7,369.50 7,430.00 7,369.50 599.12M    0.50% 2021-09-10
## 128 Sep 09, 2021 7369.5 7,512.00 7,512.00 7,344.30 740.25M   -1.90% 2021-09-09
## 129 Sep 08, 2021 7512.0 7,530.30 7,530.30 7,476.30 688.88M   -0.24% 2021-09-08
## 130 Sep 07, 2021 7530.3 7,528.50 7,536.90 7,487.80 591.48M    0.02% 2021-09-07
## 131 Sep 06, 2021 7528.5 7,522.90 7,528.50 7,440.10 807.64M    0.07% 2021-09-06
## 132 Sep 03, 2021 7522.9 7,485.70 7,539.00 7,485.70 586.72M    0.50% 2021-09-03
## 133 Sep 02, 2021 7485.7 7,527.10 7,527.10 7,437.20 631.41M   -0.55% 2021-09-02
## 134 Sep 01, 2021 7527.1 7,534.90 7,534.90 7,462.20 618.48M   -0.10% 2021-09-01
## 135 Aug 31, 2021 7534.9 7,504.50 7,555.90 7,504.50 806.31M    0.41% 2021-08-31
## 136 Aug 30, 2021 7504.5 7,488.30 7,528.30 7,476.00 648.07M    0.22% 2021-08-30
## 137 Aug 27, 2021 7488.3 7,491.20 7,494.50 7,465.00 756.50M   -0.04% 2021-08-27
## 138 Aug 26, 2021 7491.2 7,531.90 7,531.90 7,477.80 771.81M   -0.54% 2021-08-26
## 139 Aug 25, 2021 7531.9 7,503.00 7,542.40 7,503.00 710.52M    0.39% 2021-08-25
## 140 Aug 24, 2021 7503.0 7,489.90 7,524.40 7,489.90 685.47M    0.17% 2021-08-24
## 141 Aug 23, 2021 7489.9 7,460.90 7,496.30 7,460.90 622.95M    0.39% 2021-08-23
## 142 Aug 20, 2021 7460.9 7,464.60 7,512.00 7,453.60 712.73M   -0.05% 2021-08-20
## 143 Aug 19, 2021 7464.6 7,502.10 7,502.10 7,429.20 747.63M   -0.50% 2021-08-19
## 144 Aug 18, 2021 7502.1 7,511.00 7,532.90 7,470.50 653.54M   -0.12% 2021-08-18
## 145 Aug 17, 2021 7511.0 7,582.50 7,582.50 7,494.40 559.15M   -0.94% 2021-08-17
## 146 Aug 16, 2021 7582.5 7,628.90 7,629.00 7,582.50 647.00M   -0.61% 2021-08-16
## 147 Aug 13, 2021 7628.9 7,588.20 7,632.80 7,585.90 605.05M    0.54% 2021-08-13
## 148 Aug 12, 2021 7588.2 7,584.30 7,608.60 7,571.50 627.84M    0.05% 2021-08-12
## 149 Aug 11, 2021 7584.3 7,562.60 7,615.10 7,562.60 643.15M    0.29% 2021-08-11
## 150 Aug 10, 2021 7562.6 7,538.40 7,576.30 7,537.10 525.15M    0.32% 2021-08-10
## 151 Aug 09, 2021 7538.4 7,538.40 7,567.00 7,532.80 526.82M    0.00% 2021-08-09
## 152 Aug 06, 2021 7538.4 7,511.10 7,538.40 7,497.30 539.38M    0.36% 2021-08-06
## 153 Aug 05, 2021 7511.1 7,503.20 7,526.40 7,493.00 510.28M    0.11% 2021-08-05
## 154 Aug 04, 2021 7503.2 7,474.50 7,509.20 7,474.50 499.67M    0.38% 2021-08-04
## 155 Aug 03, 2021 7474.5 7,491.40 7,495.90 7,455.50 623.33M   -0.23% 2021-08-03
## 156 Aug 02, 2021 7491.4 7,415.90 7,506.30 7,414.90 576.29M    1.34% 2021-08-02
## 157 Jul 30, 2021 7392.6 7,417.40 7,437.70 7,387.90 632.53M   -0.33% 2021-07-30
## 158 Jul 29, 2021 7417.4 7,379.30 7,418.70 7,379.30 514.88M    0.52% 2021-07-29
## 159 Jul 28, 2021 7379.3 7,431.40 7,432.40 7,368.30 585.95M   -0.70% 2021-07-28
## 160 Jul 26, 2021 7394.3 7,394.40 7,417.60 7,389.30 548.09M   -0.00% 2021-07-26
## 161 Jul 23, 2021 7394.4 7,386.40 7,398.70 7,357.00 493.58M    0.11% 2021-07-23
## 162 Jul 22, 2021 7386.4 7,308.70 7,386.40 7,308.70 582.73M    1.06% 2021-07-22
## 163 Jul 21, 2021 7308.7 7,252.20 7,354.80 7,252.20 581.30M    0.78% 2021-07-21
## 164 Jul 20, 2021 7252.2 7,286.00 7,286.00 7,205.00 644.64M   -0.46% 2021-07-20
## 165 Jul 19, 2021 7286.0 7,348.10 7,348.10 7,249.20 496.36M   -0.85% 2021-07-19
## 166 Jul 16, 2021 7348.1 7,335.90 7,348.10 7,316.30 592.38M    0.17% 2021-07-16
## 167 Jul 15, 2021 7335.9 7,354.70 7,365.70 7,322.90 643.69M   -0.26% 2021-07-15
## 168 Jul 14, 2021 7354.7 7,332.10 7,368.50 7,326.50 561.31M    0.31% 2021-07-14
## 169 Jul 13, 2021 7332.1 7,333.50 7,382.20 7,332.10 616.75M   -0.02% 2021-07-13
## 170 Jul 12, 2021 7333.5 7,273.30 7,353.40 7,273.30 471.35M    0.83% 2021-07-12
## 171 Jul 09, 2021 7273.3 7,341.40 7,341.40 7,226.00 621.91M   -0.93% 2021-07-09
## 172 Jul 08, 2021 7341.4 7,326.90 7,381.90 7,326.90 573.96M    0.20% 2021-07-08
## 173 Jul 07, 2021 7326.9 7,261.80 7,332.40 7,255.50 706.86M    0.90% 2021-07-07
## 174 Jul 06, 2021 7261.8 7,315.00 7,346.20 7,261.80 552.71M   -0.73% 2021-07-06
## 175 Jul 05, 2021 7315.0 7,308.60 7,343.70 7,306.70 548.27M    0.09% 2021-07-05
## 176 Jul 02, 2021 7308.6 7,265.60 7,312.30 7,265.60 501.02M    0.59% 2021-07-02
## 177 Jul 01, 2021 7265.6 7,313.00 7,317.20 7,265.60 573.31M   -0.65% 2021-07-01
## 178 Jun 30, 2021 7313.0 7,301.20 7,370.10 7,301.20 675.23M    0.16% 2021-06-30
## 179 Jun 29, 2021 7301.2 7,308.00 7,308.00 7,241.50 514.75M   -0.08% 2021-06-29
## 180 Jun 28, 2021 7307.3 7,308.00 7,310.60 7,273.70 515.55M   -0.01% 2021-06-28
## 181 Jun 25, 2021 7308.0 7,275.30 7,325.60 7,275.30 589.93M    0.45% 2021-06-25
## 182 Jun 24, 2021 7275.3 7,298.50 7,303.80 7,256.30 583.50M   -0.32% 2021-06-24
## 183 Jun 23, 2021 7298.5 7,342.20 7,344.00 7,292.90 587.63M   -0.60% 2021-06-23
## 184 Jun 22, 2021 7342.2 7,235.30 7,365.90 7,235.30 761.07M    1.48% 2021-06-22
## 185 Jun 21, 2021 7235.3 7,368.90 7,368.90 7,216.60 607.37M   -1.81% 2021-06-21
## 186 Jun 18, 2021 7368.9 7,359.00 7,403.20 7,334.90   1.23B    0.13% 2021-06-18
## 187 Jun 17, 2021 7359.0 7,386.20 7,386.20 7,341.90 772.07M   -0.37% 2021-06-17
## 188 Jun 16, 2021 7386.2 7,379.50 7,406.20 7,372.40 663.03M    0.09% 2021-06-16
## 189 Jun 15, 2021 7379.5 7,312.30 7,398.60 7,312.30 613.74M    0.92% 2021-06-15
## 190 Jun 10, 2021 7302.5 7,270.20 7,314.60 7,265.60 630.69M    0.44% 2021-06-10
## 191 Jun 09, 2021 7270.2 7,292.60 7,334.90 7,270.20 567.09M   -0.31% 2021-06-09
## 192 Jun 08, 2021 7292.6 7,281.90 7,315.60 7,267.60 504.73M    0.15% 2021-06-08
## 193 Jun 07, 2021 7281.9 7,295.40 7,309.40 7,269.40 471.21M   -0.19% 2021-06-07
## 194 Jun 04, 2021 7295.4 7,260.10 7,300.50 7,244.30 645.70M    0.49% 2021-06-04
## 195 Jun 03, 2021 7260.1 7,217.80 7,281.80 7,217.80 592.84M    0.59% 2021-06-03
## 196 Jun 02, 2021 7217.8 7,142.60 7,218.90 7,142.60 574.83M    1.05% 2021-06-02
## 197 Jun 01, 2021 7142.6 7,161.60 7,163.00 7,117.50 414.77M   -0.27% 2021-06-01
## 198 May 31, 2021 7161.6 7,179.50 7,203.30 7,157.20 499.77M   -0.25% 2021-05-31
## 199 May 28, 2021 7179.5 7,094.90 7,186.80 7,094.90 606.89M    1.19% 2021-05-28
## 200 May 27, 2021 7094.9 7,092.50 7,118.90 7,082.40   1.40B    0.03% 2021-05-27
## 201 May 26, 2021 7092.5 7,115.20 7,136.40 7,090.10 517.99M   -0.32% 2021-05-26
## 202 May 25, 2021 7115.2 7,045.90 7,115.20 7,045.90 483.42M    0.98% 2021-05-25
## 203 May 20, 2021 7019.6 6,931.70 7,025.40 6,919.40 664.42M    1.27% 2021-05-20
## 204 May 19, 2021 6931.7 7,066.00 7,066.00 6,917.30 702.25M   -1.90% 2021-05-19
## 205 May 18, 2021 7066.0 7,023.60 7,083.60 7,023.60 524.94M    0.60% 2021-05-18
## 206 May 17, 2021 7023.6 7,014.20 7,065.70 7,014.20 453.25M    0.13% 2021-05-17
## 207 May 14, 2021 7014.2 6,982.70 7,055.70 6,982.70 520.96M    0.45% 2021-05-14
## 208 May 13, 2021 6982.7 7,044.90 7,044.90 6,966.50 617.00M   -0.88% 2021-05-13
## 209 May 12, 2021 7044.9 7,097.00 7,097.00 7,006.60 644.65M   -0.73% 2021-05-12
## 210 May 11, 2021 7097.0 7,172.80 7,172.80 7,078.00 558.92M   -1.06% 2021-05-11
## 211 May 10, 2021 7172.8 7,080.80 7,172.80 7,076.10 588.42M    1.30% 2021-05-10
## 212 May 07, 2021 7080.8 7,061.70 7,101.20 7,054.10 532.25M    0.27% 2021-05-07
## 213 May 06, 2021 7061.7 7,095.80 7,112.50 7,042.70 625.15M   -0.48% 2021-05-06
## 214 May 05, 2021 7095.8 7,067.90 7,122.00 7,054.00 510.16M    0.39% 2021-05-05
## 215 May 04, 2021 7067.9 7,028.80 7,069.50 7,028.80 484.50M    0.56% 2021-05-04
## 216 May 03, 2021 7028.8 7,025.80 7,068.40 7,022.00 468.23M    0.04% 2021-05-03
## 217 Apr 30, 2021 7025.8 7,082.30 7,082.30 7,012.80 615.51M   -0.80% 2021-04-30
## 218 Apr 29, 2021 7082.3 7,064.70 7,096.90 7,064.70 634.86M    0.25% 2021-04-29
## 219 Apr 28, 2021 7064.7 7,033.80 7,077.50 7,028.60 620.72M    0.44% 2021-04-28
## 220 Apr 27, 2021 7033.8 7,045.60 7,053.30 7,005.90 596.35M   -0.17% 2021-04-27
## 221 Apr 26, 2021 7045.6 7,060.70 7,075.20 7,045.60 427.51M   -0.21% 2021-04-26
## 222 Apr 23, 2021 7060.7 7,055.40 7,060.70 7,036.80 485.41M    0.08% 2021-04-23
## 223 Apr 22, 2021 7055.4 6,997.50 7,055.40 6,993.70 663.98M    0.83% 2021-04-22
## 224 Apr 21, 2021 6997.5 7,017.80 7,017.80 6,905.40 689.08M   -0.29% 2021-04-21
## 225 Apr 20, 2021 7017.8 7,065.60 7,067.20 7,008.80 554.64M   -0.68% 2021-04-20
## 226 Apr 19, 2021 7065.6 7,063.50 7,094.80 7,063.50 473.52M    0.03% 2021-04-19
## 227 Apr 16, 2021 7063.5 7,058.60 7,066.40 7,030.10 627.01M    0.07% 2021-04-16
## 228 Apr 15, 2021 7058.6 7,023.10 7,071.50 6,988.60 686.79M    0.51% 2021-04-15
## 229 Apr 14, 2021 7023.1 6,976.90 7,027.40 6,976.90 576.45M    0.66% 2021-04-14
## 230 Apr 13, 2021 6976.9 6,974.00 6,998.00 6,962.90 507.31M    0.04% 2021-04-13
## 231 Apr 12, 2021 6974.0 6,996.70 6,996.70 6,957.40 405.10M   -0.30% 2021-04-12
## 232 Apr 09, 2021 6995.2 6,998.80 6,998.80 6,965.50 425.91M   -0.05% 2021-04-09
## 233 Apr 08, 2021 6998.8 6,928.00 7,012.40 6,928.00 544.99M    1.02% 2021-04-08
## 234 Apr 07, 2021 6928.0 6,885.90 6,933.90 6,885.90 612.67M    0.61% 2021-04-07
## 235 Apr 06, 2021 6885.9 6,828.70 6,915.70 6,828.70 517.51M    0.84% 2021-04-06
## 236 Apr 01, 2021 6828.7 6,790.70 6,830.40 6,783.30 473.19M    0.56% 2021-04-01
## 237 Mar 31, 2021 6790.7 6,738.40 6,862.60 6,738.40 717.60M    0.78% 2021-03-31
## 238 Mar 30, 2021 6738.4 6,799.50 6,836.30 6,738.40 513.84M   -0.90% 2021-03-30
## 239 Mar 29, 2021 6799.5 6,824.20 6,860.60 6,794.10 502.07M   -0.36% 2021-03-29
## 240 Mar 26, 2021 6824.2 6,790.60 6,835.00 6,790.60 571.46M    0.49% 2021-03-26
## 241 Mar 25, 2021 6790.6 6,778.80 6,806.00 6,769.90 558.91M    0.17% 2021-03-25
## 242 Mar 24, 2021 6778.8 6,745.40 6,799.10 6,735.60 503.00M    0.50% 2021-03-24
## 243 Mar 23, 2021 6745.4 6,752.50 6,786.20 6,741.30 518.77M   -0.11% 2021-03-23
## 244 Mar 22, 2021 6752.5 6,708.20 6,763.40 6,688.20 472.04M    0.66% 2021-03-22
## 245 Mar 19, 2021 6708.2 6,745.90 6,745.90 6,673.70   1.25B   -0.56% 2021-03-19
## 246 Mar 18, 2021 6745.9 6,795.20 6,806.20 6,744.50 715.60M   -0.73% 2021-03-18
## 247 Mar 17, 2021 6795.2 6,827.10 6,827.10 6,761.40 603.82M   -0.47% 2021-03-17
## 248 Mar 16, 2021 6827.1 6,773.00 6,858.90 6,767.80 587.15M    0.80% 2021-03-16
## 249 Mar 15, 2021 6773.0 6,766.80 6,793.50 6,727.50 484.55M    0.09% 2021-03-15
## 250 Mar 12, 2021 6766.8 6,713.90 6,783.00 6,713.90 468.30M    0.79% 2021-03-12
## 251 Mar 11, 2021 6713.9 6,714.10 6,756.70 6,648.60 665.89M   -0.00% 2021-03-11
## 252 Mar 10, 2021 6714.1 6,771.20 6,806.50 6,714.10 619.77M   -0.84% 2021-03-10
## 253 Mar 09, 2021 6771.2 6,739.60 6,810.00 6,739.60 733.62M    0.47% 2021-03-09
## 254 Mar 08, 2021 6739.6 6,710.80 6,835.60 6,710.80 609.64M    0.43% 2021-03-08
## 255 Mar 05, 2021 6710.8 6,760.70 6,760.70 6,660.50 702.40M   -0.74% 2021-03-05
## 256 Mar 04, 2021 6760.7 6,818.00 6,818.00 6,709.00 737.49M   -0.84% 2021-03-04
## 257 Mar 03, 2021 6818.0 6,762.30 6,818.80 6,762.30 591.22M    0.82% 2021-03-03
## 258 Mar 02, 2021 6762.3 6,789.60 6,860.70 6,762.30 739.31M   -0.40% 2021-03-02
## 259 Mar 01, 2021 6789.6 6,673.30 6,790.10 6,672.20 734.33M    1.74% 2021-03-01
## 260 Feb 26, 2021 6673.3 6,834.00 6,834.00 6,658.90   1.03B   -2.35% 2021-02-26
## 261 Feb 25, 2021 6834.0 6,777.80 6,857.30 6,777.80 806.72M    0.83% 2021-02-25
## 262 Feb 24, 2021 6777.8 6,839.20 6,839.20 6,762.60 677.31M   -0.90% 2021-02-24
## 263 Feb 23, 2021 6839.2 6,780.90 6,839.20 6,765.30 806.33M    0.86% 2021-02-23
## 264 Feb 22, 2021 6780.9 6,793.80 6,824.80 6,774.30 653.24M   -0.19% 2021-02-22
##     new_cases new_deaths  location new_vaccinations new_tests population
## 1       47988         25 Australia            84708    129628   25788217
## 2       66782         33 Australia            78602    113852   25788217
## 3       59704         31 Australia            71214    146305   25788217
## 4       57556         23 Australia            82603     39917   25788217
## 5       53063         27 Australia            86498     81698   25788217
## 6       46952         31 Australia            99204    101292   25788217
## 7       49166         29 Australia            91189    121039   25788217
## 8       57148         24 Australia            92558    102020   25788217
## 9       54309         23 Australia            96012     77557   25788217
## 10      28803         25 Australia            73534     69645   25788217
## 11      35077         21 Australia           106393    110525   25788217
## 12      31689         30 Australia            92674     94108   25788217
## 13      36335         25 Australia            90219     99999   25788217
## 14      30691         31 Australia            89302     71498   25788217
## 15      44962         39 Australia            77637     65021   25788217
## 16      22716         48 Australia            90681     92085   25788217
## 17      25159         38 Australia            85961     94936   25788217
## 18      40012         47 Australia            86384     98154   25788217
## 19      21755         59 Australia            90001     69785   25788217
## 20      25418         39 Australia           102040     58369   25788217
## 21      19803         36 Australia           121620    100549   25788217
## 22      26670         33 Australia           123136     89532   25788217
## 23      24852         37 Australia           123985    100703   25788217
## 24      24394         62 Australia           160237     57711   25788217
## 25      30913         60 Australia           166577     67597   25788217
## 26      22564         64 Australia           202409    100310   25788217
## 27      31250         57 Australia           201007     94636   25788217
## 28      11483         49 Australia           213814    113891   25788217
## 29      31510         71 Australia           219558     86132   25788217
## 30      27373         54 Australia           215669     82525   25788217
## 31      65980        155 Australia           306420    102759   25788217
## 32      66223         87 Australia           303363    140456   25788217
## 33      52920         69 Australia           321385    134310   25788217
## 34      58022         82 Australia           320356    110638   25788217
## 35      43166         65 Australia           334030    187145   25788217
## 36      63941         88 Australia           351226    186541   25788217
## 37      67604         47 Australia           331186    213047   25788217
## 38      84615         70 Australia           320525    165245   25788217
## 39      84355         81 Australia           327447    199188   25788217
## 40      86288         49 Australia           337813    252188   25788217
## 41     128185         50 Australia           345598    219303   25788217
## 42     175271         57 Australia           345035    288897   25788217
## 43      84408         50 Australia           326257    215158   25788217
## 44      93806         28 Australia           287855    221101   25788217
## 45      98092         16 Australia           265220    258550   25788217
## 46      78229         19 Australia           272293    248366   25788217
## 47      75426         12 Australia           261562    219727   25788217
## 48      71495         20 Australia           211902    212046   25788217
## 49      30363         14 Australia                0    255441   25788217
## 50      33811         15 Australia           159880    267028   25788217
## 51      21369         14 Australia           148570    301897   25788217
## 52       9855          6 Australia                0    321830   25788217
## 53       8218          9 Australia           192352    319186   25788217
## 54       8357         11 Australia           215290    310301   25788217
## 55       5531          8 Australia           206796    270702   25788217
## 56       4492          8 Australia           194274    273977   25788217
## 57       4017          8 Australia           208631    252745   25788217
## 58       3802          8 Australia           198405    268541   25788217
## 59       3423          9 Australia           183047    222651   25788217
## 60       2809          4 Australia           161527    178487   25788217
## 61       1970          7 Australia           153523    163838   25788217
## 62       1757         16 Australia            86258    189327   25788217
## 63       1711          2 Australia            96554    189466   25788217
## 64       1654         10 Australia            95133    207445   25788217
## 65       1705          7 Australia            88333    153592   25788217
## 66       1434          9 Australia            86815    148163   25788217
## 67       1708          9 Australia            98274    184165   25788217
## 68       1522         12 Australia            98936    177193   25788217
## 69       1702         10 Australia            92675    187923   25788217
## 70       1417          5 Australia            92151    128373   25788217
## 71       1099          9 Australia            95215    123218   25788217
## 72       1480          5 Australia           104321    180767   25788217
## 73       1627          7 Australia           105906    174186   25788217
## 74       1527          5 Australia           105772    183383   25788217
## 75       1464          5 Australia           108657    120302   25788217
## 76       1002         20 Australia           142490    119615   25788217
## 77       1360          5 Australia           120321    172738   25788217
## 78       1505         11 Australia           127813    176483   25788217
## 79       1267         15 Australia           128398    184546   25788217
## 80       1228          9 Australia           136366    144221   25788217
## 81       1014         10 Australia           140126    119223   25788217
## 82       1452          4 Australia           160104    167736   25788217
## 83       1414         11 Australia           161921    169608   25788217
## 84       1556          4 Australia           153985    191821   25788217
## 85       1201         17 Australia           149289    136463   25788217
## 86       1292         14 Australia           155259    122014   25788217
## 87       1534         10 Australia           197031    170379   25788217
## 88       1580         14 Australia           194415    160355   25788217
## 89       1537         13 Australia           194829    201945   25788217
## 90       1111         12 Australia           164592    125068   25788217
## 91       1135         13 Australia           222150    126983   25788217
## 92       1589         14 Australia           206993    173166   25788217
## 93       1886         12 Australia           223461    191835   25788217
## 94       2038         27 Australia           217681    195419   25788217
## 95       1850         16 Australia           224311    149428   25788217
## 96       1786          5 Australia           232426    139527   25788217
## 97       1883         11 Australia           270165    171882   25788217
## 98       2547         21 Australia           277103    188693   25788217
## 99       2544         13 Australia           274371    192296   25788217
## 100      2097         19 Australia           275350    179749   25788217
## 101      2038         15 Australia           277427    152710   25788217
## 102      2311          8 Australia           298975    185923   25788217
## 103      2532         11 Australia           312722    197156   25788217
## 104      2744         18 Australia           318459    210259   25788217
## 105      2029         17 Australia           336239    178619   25788217
## 106      1848         13 Australia           312260    185405   25788217
## 107      2515         16 Australia           330414    211751   25788217
## 108      2524         16 Australia           330622    220866   25788217
## 109      2216         10 Australia           335452    189565   25788217
## 110      2026         22 Australia           349856    173171   25788217
## 111      2389         11 Australia           185229    169509   25788217
## 112      2335         10 Australia           325998    203061   25788217
## 113      2058         20 Australia           340206    214250   25788217
## 114      2400         12 Australia           307852    210417   25788217
## 115      1812         23 Australia           330962    203804   25788217
## 116      1880         11 Australia           302732    170072   25788217
## 117      1865         12 Australia           310453    215327   25788217
## 118      1764         12 Australia           346941    229864   25788217
## 119      1809         10 Australia           336435    221979   25788217
## 120      1659          8 Australia           331055    194751   25788217
## 121      1582         11 Australia           320356    203725   25788217
## 122      1856          7 Australia           337449    331422   25788217
## 123      1800         13 Australia           301520    223818   25788217
## 124      1856         12 Australia           305082    223742   25788217
## 125      1643         14 Australia           301560    187043   25788217
## 126      1578          4 Australia           340310    220995   25788217
## 127      2075          8 Australia           302779    200659   25788217
## 128      1870         10 Australia           318262    240435   25788217
## 129      1725          6 Australia           310741    203949   25788217
## 130      1696          8 Australia           293861    172589   25788217
## 131      1466          8 Australia           258277    206705   25788217
## 132      1741          4 Australia           311513    235695   25788217
## 133      1648         13 Australia           300673    203243   25788217
## 134      1467          7 Australia           329845    266169   25788217
## 135      1225          6 Australia           333975    210646   25788217
## 136      1257          3 Australia           276585    219520   25788217
## 137      1119          2 Australia           315575    201823   25788217
## 138       976          2 Australia           306438    238317   25788217
## 139      1115          3 Australia           333993    238375   25788217
## 140       971          2 Australia           306095    212116   25788217
## 141       834          0 Australia           289175    389580   25788217
## 142       891          2 Australia           309846    212663   25788217
## 143       707          4 Australia           302685    206177   25788217
## 144       747          2 Australia           308344    180161   25788217
## 145       677          3 Australia           273299    212991   25788217
## 146       482          1 Australia           278827    217648   25788217
## 147       493          4 Australia           279022    217811   25788217
## 148       416          2 Australia           269793    243144   25788217
## 149       372          2 Australia           262964    205968   25788217
## 150       367          2 Australia           266264    160681   25788217
## 151       380          4 Australia           217785    216451   25788217
## 152       353          6 Australia           235991    207740   25788217
## 153       307          1 Australia           239906    202641   25788217
## 154       302          5 Australia           222697    205159   25788217
## 155       253          2 Australia           219092    180613   25788217
## 156       224          0 Australia           189313    183370   25788217
## 157       221          1 Australia           210770    176053   25788217
## 158       183          0 Australia           204939    204244   25788217
## 159       253          2 Australia           223220    168966   25788217
## 160       184          4 Australia           179667    164158   25788217
## 161       171          0 Australia           184359    179490   25788217
## 162       156          1 Australia           188180    174432   25788217
## 163       160          0 Australia           194940    180180   25788217
## 164       146          0 Australia           173432    137376   25788217
## 165       104          1 Australia           167185    158808   25788217
## 166       145          1 Australia           162759    140981   25788217
## 167       113          0 Australia           171692    113068   25788217
## 168        84          0 Australia           170025    126082   25788217
## 169       106          0 Australia           168921    102796   25788217
## 170       102          1 Australia           146118    130133   25788217
## 171        64          0 Australia           144830     98104   25788217
## 172        48          0 Australia           156750     99235   25788217
## 173        42          0 Australia           149630     82037   25788217
## 174        30          0 Australia           159464     93563   25788217
## 175        27          0 Australia           135044    116934   25788217
## 176        50          0 Australia           154817    162784   25788217
## 177        41          0 Australia           185318    155772   25788217
## 178        41          0 Australia            96951    150302   25788217
## 179        42          0 Australia           144885    126945   25788217
## 180        32          0 Australia           126034    100163   25788217
## 181        33          0 Australia           119973     91915   25788217
## 182        20          0 Australia           138434     93522   25788217
## 183        24          0 Australia           138922     96664   25788217
## 184        14          0 Australia           140892     59835   25788217
## 185        11          0 Australia           128643     60188   25788217
## 186        13          0 Australia           130810     75644   25788217
## 187         8          0 Australia           141336     70468   25788217
## 188        15          0 Australia           136010     47437   25788217
## 189        13          0 Australia           152075     42936   25788217
## 190         3          0 Australia           153338     67478   25788217
## 191        15          0 Australia           142808     73284   25788217
## 192         5          0 Australia           140885     54037   25788217
## 193        14          0 Australia           127564     89000   25788217
## 194        16          0 Australia           141245     89027   25788217
## 195         4          0 Australia           143659     96003   25788217
## 196        13          0 Australia           141259     93405   25788217
## 197         6          0 Australia           138705     68241   25788217
## 198        13          0 Australia           119139     76663   25788217
## 199         9          0 Australia           121610     95647   25788217
## 200        11          0 Australia           124871     81048   25788217
## 201        17          0 Australia           111388     61494   25788217
## 202        17          0 Australia           104658     35564   25788217
## 203         4          0 Australia           101146     55335   25788217
## 204         3          0 Australia            92874     58524   25788217
## 205         9          0 Australia            95530     39354   25788217
## 206         5          0 Australia            83187     36132   25788217
## 207         7          0 Australia            76153     54093   25788217
## 208         2          0 Australia            85874     55232   25788217
## 209         9          0 Australia            82284     61475   25788217
## 210         8          0 Australia            76379     35191   25788217
## 211         7          0 Australia            72886     36799   25788217
## 212        13          0 Australia            73194     48969   25788217
## 213         9          0 Australia            81002     49746   25788217
## 214        19          0 Australia            77215     53237   25788217
## 215        15          0 Australia            79345     30733   25788217
## 216        12          0 Australia            56354     28900   25788217
## 217        13          0 Australia            55300     44266   25788217
## 218        25          0 Australia            67259     50422   25788217
## 219        23          0 Australia            82741     59461   25788217
## 220        33          0 Australia            60207     42490   25788217
## 221        37          0 Australia            31852     40627   25788217
## 222        15          0 Australia            58446     33457   25788217
## 223        44          0 Australia            69903     37186   25788217
## 224        18          0 Australia            67591     37749   25788217
## 225        20          0 Australia            64821     27878   25788217
## 226        23          0 Australia            66730     28648   25788217
## 227        15          0 Australia            53981     39152   25788217
## 228        15          0 Australia            61272     44406   25788217
## 229        19          0 Australia            63633     45793   25788217
## 230        21          0 Australia            60991     28512   25788217
## 231        10          1 Australia            56379     32317   25788217
## 232         6          0 Australia            61355     50988   25788217
## 233         5          0 Australia            81297     44549   25788217
## 234         6          0 Australia            75880     32561   25788217
## 235        14          0 Australia            65351     28581   25788217
## 236        11          0 Australia            79283     85667   25788217
## 237        18          0 Australia            73979     84676   25788217
## 238         8          0 Australia            72826     44989   25788217
## 239        20          0 Australia            55764     45086   25788217
## 240        13          0 Australia            46310     38818   25788217
## 241         9          0 Australia            51745     39005   25788217
## 242         9          0 Australia            49908     47726   25788217
## 243        10          0 Australia            46000     29572   25788217
## 244         5          0 Australia            30542     30548   25788217
## 245         9          0 Australia            13077     44082   25788217
## 246        17          0 Australia            14697     50496   25788217
## 247        12          0 Australia            22500     54260   25788217
## 248        17          0 Australia            21120     33843   25788217
## 249         7          0 Australia            17656     32312   25788217
## 250        10          0 Australia            24185     45015   25788217
## 251        12          0 Australia            10109     51011   25788217
## 252        16          0 Australia            16672     47309   25788217
## 253        13          0 Australia            13420     27603   25788217
## 254        15          0 Australia             8539     35146   25788217
## 255         8          0 Australia             5073     47635   25788217
## 256        14          0 Australia            10858     49049   25788217
## 257        11          0 Australia             9939     52921   25788217
## 258        10          0 Australia             9163     39974   25788217
## 259         8          0 Australia             7277     24226   25788217
## 260         8          0 Australia             6490     47899   25788217
## 261        10          0 Australia             6881     49940   25788217
## 262         8          0 Australia             9715     56776   25788217
## 263         2          0 Australia             4125     43521   25788217
## 264         7          0 Australia             2769     36501   25788217
##     population_density
## 1                3.202
## 2                3.202
## 3                3.202
## 4                3.202
## 5                3.202
## 6                3.202
## 7                3.202
## 8                3.202
## 9                3.202
## 10               3.202
## 11               3.202
## 12               3.202
## 13               3.202
## 14               3.202
## 15               3.202
## 16               3.202
## 17               3.202
## 18               3.202
## 19               3.202
## 20               3.202
## 21               3.202
## 22               3.202
## 23               3.202
## 24               3.202
## 25               3.202
## 26               3.202
## 27               3.202
## 28               3.202
## 29               3.202
## 30               3.202
## 31               3.202
## 32               3.202
## 33               3.202
## 34               3.202
## 35               3.202
## 36               3.202
## 37               3.202
## 38               3.202
## 39               3.202
## 40               3.202
## 41               3.202
## 42               3.202
## 43               3.202
## 44               3.202
## 45               3.202
## 46               3.202
## 47               3.202
## 48               3.202
## 49               3.202
## 50               3.202
## 51               3.202
## 52               3.202
## 53               3.202
## 54               3.202
## 55               3.202
## 56               3.202
## 57               3.202
## 58               3.202
## 59               3.202
## 60               3.202
## 61               3.202
## 62               3.202
## 63               3.202
## 64               3.202
## 65               3.202
## 66               3.202
## 67               3.202
## 68               3.202
## 69               3.202
## 70               3.202
## 71               3.202
## 72               3.202
## 73               3.202
## 74               3.202
## 75               3.202
## 76               3.202
## 77               3.202
## 78               3.202
## 79               3.202
## 80               3.202
## 81               3.202
## 82               3.202
## 83               3.202
## 84               3.202
## 85               3.202
## 86               3.202
## 87               3.202
## 88               3.202
## 89               3.202
## 90               3.202
## 91               3.202
## 92               3.202
## 93               3.202
## 94               3.202
## 95               3.202
## 96               3.202
## 97               3.202
## 98               3.202
## 99               3.202
## 100              3.202
## 101              3.202
## 102              3.202
## 103              3.202
## 104              3.202
## 105              3.202
## 106              3.202
## 107              3.202
## 108              3.202
## 109              3.202
## 110              3.202
## 111              3.202
## 112              3.202
## 113              3.202
## 114              3.202
## 115              3.202
## 116              3.202
## 117              3.202
## 118              3.202
## 119              3.202
## 120              3.202
## 121              3.202
## 122              3.202
## 123              3.202
## 124              3.202
## 125              3.202
## 126              3.202
## 127              3.202
## 128              3.202
## 129              3.202
## 130              3.202
## 131              3.202
## 132              3.202
## 133              3.202
## 134              3.202
## 135              3.202
## 136              3.202
## 137              3.202
## 138              3.202
## 139              3.202
## 140              3.202
## 141              3.202
## 142              3.202
## 143              3.202
## 144              3.202
## 145              3.202
## 146              3.202
## 147              3.202
## 148              3.202
## 149              3.202
## 150              3.202
## 151              3.202
## 152              3.202
## 153              3.202
## 154              3.202
## 155              3.202
## 156              3.202
## 157              3.202
## 158              3.202
## 159              3.202
## 160              3.202
## 161              3.202
## 162              3.202
## 163              3.202
## 164              3.202
## 165              3.202
## 166              3.202
## 167              3.202
## 168              3.202
## 169              3.202
## 170              3.202
## 171              3.202
## 172              3.202
## 173              3.202
## 174              3.202
## 175              3.202
## 176              3.202
## 177              3.202
## 178              3.202
## 179              3.202
## 180              3.202
## 181              3.202
## 182              3.202
## 183              3.202
## 184              3.202
## 185              3.202
## 186              3.202
## 187              3.202
## 188              3.202
## 189              3.202
## 190              3.202
## 191              3.202
## 192              3.202
## 193              3.202
## 194              3.202
## 195              3.202
## 196              3.202
## 197              3.202
## 198              3.202
## 199              3.202
## 200              3.202
## 201              3.202
## 202              3.202
## 203              3.202
## 204              3.202
## 205              3.202
## 206              3.202
## 207              3.202
## 208              3.202
## 209              3.202
## 210              3.202
## 211              3.202
## 212              3.202
## 213              3.202
## 214              3.202
## 215              3.202
## 216              3.202
## 217              3.202
## 218              3.202
## 219              3.202
## 220              3.202
## 221              3.202
## 222              3.202
## 223              3.202
## 224              3.202
## 225              3.202
## 226              3.202
## 227              3.202
## 228              3.202
## 229              3.202
## 230              3.202
## 231              3.202
## 232              3.202
## 233              3.202
## 234              3.202
## 235              3.202
## 236              3.202
## 237              3.202
## 238              3.202
## 239              3.202
## 240              3.202
## 241              3.202
## 242              3.202
## 243              3.202
## 244              3.202
## 245              3.202
## 246              3.202
## 247              3.202
## 248              3.202
## 249              3.202
## 250              3.202
## 251              3.202
## 252              3.202
## 253              3.202
## 254              3.202
## 255              3.202
## 256              3.202
## 257              3.202
## 258              3.202
## 259              3.202
## 260              3.202
## 261              3.202
## 262              3.202
## 263              3.202
## 264              3.202

Initial analysis for new_cases_smoothed

covid_temp <- covid_clean
covid_temp$month <- strftime(covid_temp$date, "%m")
covid_temp$year  <- strftime(covid_temp$date, "%Y")

covid_temp_new_case_aggregate <- aggregate(new_cases_smoothed~month+year, 
                  covid_temp,
                  FUN = mean)
covid_temp_new_case_aggregate$month_year <- paste(covid_temp_new_case_aggregate$month, covid_temp_new_case_aggregate$year)
ggplot(covid_temp_new_case_aggregate, aes(x= new_cases_smoothed, y= month_year)) +
  geom_bar(stat = 'identity') + ggtitle("Average New Cases (Smoothed) per Month") +
  ylab("month year") + xlab("average new cases")
  • Analysis: From the above bar plot, we set different months of 2021 and 2022 as y-axis and the average smoothed new cases for each month as x-axis. We can easily know that smoothed new cases in most months are less than 30,000 except in Jan, 2022, which is nearly 50,000. So the data of new_cases_smoothed is evenly distributed and has a small standard deviation.

Initial analysis for new_people_vaccinated_smoothed

covid_temp = covid_clean %>% select(date,new_people_vaccinated_smoothed)
covid_temp$date <- as.Date(covid_temp$date, format = "%Y-%m-%d")
covid_temp_new_vaccinated_aggregate = covid_temp %>% mutate(month_year = as.character(format(date, "%m-%Y"))) %>%
          group_by(month_year) %>%
  summarise(date=date[1], number = mean(new_people_vaccinated_smoothed))
covid_temp_new_vaccinated_aggregate  
ggplot(covid_temp_new_vaccinated_aggregate, aes(x = number, y= month_year)) +
  geom_bar(stat = 'identity') + ggtitle("Average New People Vaccinated (Smoothed) per Month") +
  ylab("month year") + xlab("average new people vaccinated")
  • Analysis: Most number show above are larger than 100000. Many people get vaccinated in July, August and September in 2021, and less people get vaccinated before and after that period which is reasonable since it takes time to invent and promote new vaccines.

Q2 a

Price ~ new_cases

#glimpse(df_aus)
df_aus_subset = df_aus %>% select(Price, new_cases)
view(df_aus_subset)

M0 = lm(Price ~ new_cases, data = df_aus_subset) # Null model
summary(M0)
## 
## Call:
## lm(formula = Price ~ new_cases, data = df_aus_subset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -549.62 -157.03   60.97  163.28  405.41 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 7.223e+03  1.525e+01 473.504   <2e-16 ***
## new_cases   1.166e-03  5.762e-04   2.024    0.044 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 226.3 on 262 degrees of freedom
## Multiple R-squared:  0.0154, Adjusted R-squared:  0.01164 
## F-statistic: 4.097 on 1 and 262 DF,  p-value: 0.04396
  • Analysis(Paul and Christin): We build a linear model here to explore relationship between stock prices and new cases of covid-19. The dependent variable here is stock prices while independent variable is new cases. Although the r-squared value is quite low and Residual standard error is a bit high, which indicates our model is not good, the p-value for new cases is 0.044( < 0.05), so it is a significantly influence Price. The final model we get is \(\text{Price} = 1.166e^{-03}\times \text{new_cases} + 7.223e^{03}\).

Scatter Plot for price and new_cases

df_aus = df_aus[order(as.Date(df_aus$date, format="%d/%m/%Y")),]

df_aus$logPrice = log(df_aus$Price)
colnames(df_aus)[7] = 'Change'

#write.csv(df_aus,"df_aus.csv")
y <- df_aus$Price
x <- df_aus$new_cases

plot(x, y, main = "Price ~ New Cases",
     ylab = "Price", xlab = "new_cases",
     pch = 19, frame = FALSE)
# Add regression line
plot(x, y, main = "Price ~ New Cases",
     ylab = "Price", xlab = "new_cases",
     pch = 19, frame = FALSE)
abline(lm(y ~ x, data = df_aus), col = "blue")

Price ~ new_vaccinations

# df_aus_subset = df_aus %>% select(Price, new_deaths) %>% filter(new_deaths != 0)

New deaths and prices

df_aus_subset = df_aus %>% select(Price, new_deaths, new_vaccinations, new_cases)
view(df_aus_subset)

M1 = lm(Price ~ poly(new_vaccinations, degree=2), data=df_aus_subset)
summary(M1)
## 
## Call:
## lm(formula = Price ~ poly(new_vaccinations, degree = 2), data = df_aus_subset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -555.39  -72.58   -0.10   77.58  697.87 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                          7235.494      8.446  856.70   <2e-16 ***
## poly(new_vaccinations, degree = 2)1  2467.685    137.228   17.98   <2e-16 ***
## poly(new_vaccinations, degree = 2)2 -1620.816    137.228  -11.81   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 137.2 on 261 degrees of freedom
## Multiple R-squared:  0.6394, Adjusted R-squared:  0.6367 
## F-statistic: 231.4 on 2 and 261 DF,  p-value: < 2.2e-16
ggplot(data = df_aus_subset, mapping = aes(x = Price, y = new_deaths)) + geom_point() + ggtitle("Prices of stocks and against new death") + ylab("New deaths") + xlab("Price")

Price ~ new_vaccinations

ggplot(data = df_aus, mapping = aes(x = new_vaccinations, y = Price)) + geom_point() + ggtitle("Prices of stocks against new vaccinations") + xlab("New vaccinations") + ylab("Price")

Price ~ New tests

df_aus_subset = df_aus %>% select(Price, new_tests)
view(df_aus_subset)
M2 = lm(Price ~ poly(new_tests, degree=2), data=df_aus_subset)
summary(M2)
## 
## Call:
## lm(formula = Price ~ poly(new_tests, degree = 2), data = df_aus_subset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -469.40  -88.38   12.24   86.71  401.48 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   7235.494      8.392 862.236  < 2e-16 ***
## poly(new_tests, degree = 2)1  2767.837    136.347  20.300  < 2e-16 ***
## poly(new_tests, degree = 2)2 -1057.592    136.347  -7.757 1.97e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 136.3 on 261 degrees of freedom
## Multiple R-squared:  0.6441, Adjusted R-squared:  0.6413 
## F-statistic: 236.1 on 2 and 261 DF,  p-value: < 2.2e-16
ggplot(data = df_aus, mapping = aes(x = new_tests, y = Price)) + geom_point() + ggtitle("Prices of stocks against new tests") + xlab("New tests") + ylab("Price")

ggplot(data = df_aus, mapping = aes(x = Price, y = population)) + geom_point() + ggtitle("Prices of stocks against population") + ylab("New tests") + xlab("Price")

ggplot(data = df_aus, mapping = aes(x = Price, y = population_density)) + geom_point() + ggtitle("Prices of stocks against population density") + ylab("New tests") + xlab("Price")

ggplot(data = df_aus, mapping = aes(x = date, y = population_density)) + geom_line() + ggtitle("Prices of stocks against population density") + ylab("New tests") + xlab("Price") # bad

summary(df_aus$Price)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    6673    7067    7296    7235    7400    7629
df_aus$Price_mul30 = df_aus$Price*30
p1 = ggplot(data = df_aus) + geom_line(aes(x=date, y = new_cases), color = "red") + geom_line(aes(x=date, y = new_vaccinations), color = "light green") + geom_line(aes(x=date, y = new_tests), color = "light blue") +
  geom_line(aes(x=date, y = Price_mul30), color = "blue") + theme_bw()

df_aus$Price_mul30 = df_aus$Price*30
p1 = ggplot(data = df_aus) + geom_line(aes(x=date, y = new_cases), color = "red") + geom_line(aes(x=date, y = new_vaccinations), color = "light green") + geom_line(aes(x=date, y = new_tests), color = "light blue")+ theme_bw()
ggplotly(p1)
autoplot(M0, which = 1:2)
autoplot(M1, which = 1:2)
autoplot(M2, which = 1:2)
M = lm(logPrice ~ polym(new_tests, new_vaccinations, degree=2, raw=TRUE), data=df_aus)
summary(M)
## 
## Call:
## lm(formula = logPrice ~ polym(new_tests, new_vaccinations, degree = 2, 
##     raw = TRUE), data = df_aus)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.068522 -0.008047  0.001027  0.009477  0.044903 
## 
## Coefficients:
##                                                                 Estimate
## (Intercept)                                                    8.804e+00
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0  3.844e-07
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 -3.683e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1  5.650e-07
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 -3.685e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 -1.171e-12
##                                                               Std. Error
## (Intercept)                                                    3.484e-03
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0  5.923e-08
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0  2.030e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1  4.766e-08
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1  2.215e-13
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2  1.302e-13
##                                                                t value Pr(>|t|)
## (Intercept)                                                   2526.972  < 2e-16
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0    6.491 4.34e-10
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0   -1.814   0.0708
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1   11.856  < 2e-16
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1   -1.664   0.0974
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2   -8.993  < 2e-16
##                                                                  
## (Intercept)                                                   ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.0 ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)2.0 .  
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.1 ***
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)1.1 .  
## polym(new_tests, new_vaccinations, degree = 2, raw = TRUE)0.2 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01542 on 258 degrees of freedom
## Multiple R-squared:  0.7704, Adjusted R-squared:  0.7659 
## F-statistic: 173.1 on 5 and 258 DF,  p-value: < 2.2e-16
ggplot(data = df_aus) + geom_point(aes(x = new_tests, y = logPrice), color = "light blue") +
  geom_point(aes(x = new_vaccinations, y = logPrice), color = "light green") +
  ggtitle("Prices of stocks against new tests and new_vaccinations") + ylab("Price") +theme_classic()

  • The behaviour of a dependent variable is explained by a linear, or curvilinear, additive relationship between the dependent variable and a set of k independent variables (xi, i=1 to k).
  • The relationship between the dependent variable and any independent variable is linear or curvilinear.
  • The independent variables do no depend on each other too.
  • The errors are independent, normally distributed with mean zero and a constant variance.